1. Field of the Invention
The present invention relates generally to offshore wind energy development, and in particular, to a method, apparatus, and article of manufacture for determining a fundamental dataset of offshore wind climatology at 1-km posting, based on the decadal satellite scatterometer data record from the entire lifetime of the QuikSCAT mission.
2. Description of the Related Art
(Note: This application references a number of different publications as indicated throughout the specification by reference numbers enclosed in brackets, e.g., [x]. A list of these different publications ordered according to these reference numbers can be found below in the section entitled “References.” Each of these publications is incorporated by reference herein.)
Energy is a critical issue facing all from local to regional, national, and international levels. With recent development of wind tower technologies, offshore wind energy emerges as an important component of the global total renewable energy portfolio. Recognizing both the value and the complexity of offshore wind energy development, energy communities, commissions, and consortiums have been formed in many regions, which identify long-term high-resolution wind measurements as critical data to evaluate and select locations appropriate for wind farming. Thus, high-resolution wind measurements have a crucial role in addressing renewable energy and climate change. In this regard, to determine where to best locate an offshore wind farm, an accurate determination/measurement of the location of offshore winds is necessary.
The National Renewable Energy Laboratory (NREL) classifies wind power density into seven classes [1] in which Class 4 or above is appropriate for wind farm development. For the estimation of the NREL power density classes, accurate long-term historical wind data are required. While model analyses are available in a number of selected regions, there are discrepancies among models and significant land contamination effects near land-sea boundaries [2]. Moreover, it is recommended that model estimates at any location be confirmed by measurements.
One method used to confirm such measurements utilizes buoys at designated locations to obtained point measurements that may not represent wind conditions in the surrounding area. In this regard, buoy measurements are obtained at a very limited number of locations and provide point data that do not represent the wind distribution over the region. For example, the buoy size itself is likely only 10-20 feet and only measures wind directly in the vicinity of the buoy. Accordingly, buoy calculations are not accurate or truly representative of wind as the distance from the buoy increases. While additional buoys may be utilized, they cannot be placed every kilometer over the entire ocean surface and still only provide limited point data. If a buoy is deployed in every 1 km2 across the world ocean, over 360 million buoys are needed. Assuming an impossibly low cost of $1,000 for each buoy per year, the total cost would amount to $3.6 trillion for a decade of data. Therefore, the extreme costs of manufacturing, deploying, maintaining, and replenishing such a global massive buoy system over a decade are prohibitive let alone any other associated issues.
Wind speed can be measured by satellite synthetic aperture radars; however, data are limited in accuracy, in time, and in space. Routine measurements of surface wind fields over global waters have been demonstrated by satellite scatterometer missions, where stable and accurate radars provide coarse resolutions [3-6]. In such missions, a wind scatterometer determines the normalized radar cross section (sigma) of the surface by transmitting a pulse of microwave energy towards the Earth's surface and measuring the energy scattered backward (backscatter) to the scatterometer. By combining the sigma measurements from different azimuth angles, the near-surface wind vector over the ocean's surface can be determined using a geophysical model function that relates wind and backscatter. However, due to the original coarse resolution (e.g., 25 km) and land contamination effects, standard wind products from the scatterometer missions are not valid by 2 pixels or 50 km away from shore.
While it is desirable for a wind farm to locate at some distance (˜10 km) away from shore for higher and steadier wind speeds and because of issues related to ocean view, noise, navigation, security, and environmental impacts, wind tower technologies and bathymetry may limit the distance to within a few tens of km from shore (˜50-100 km). Thus, the standard coarse low-resolution satellite wind products from satellite scatterometer data are neither useful nor applicable.
To utilize satellite scatterometer data for offshore wind energy applications, the spatial resolution must be increased significantly. In this regard, attempts have been made to reconstruct high-resolution data based on low-resolution data. Algorithms for high-resolution data reconstruction from low-resolution data are traditionally based on the deconvolution method, such as the Computer Tomography scan (CT scan) used in medical imaging, for which Hounsfield and Cormack earned the 1979 Nobel Prize in medicine. In this regard, the traditional deconvolution approach has been used to enhance resolution of radar data [7-9]. However, such approaches require that radar backscatter remains unchanged in each high-resolution pixel during the period of data acquisition used in the resolution enhancement process (similar to requiring a patient to stay still while the CT scan is made), and are not applicable to backscatter data acquired over ocean surface that are rapidly variable in time and in different azimuth angles. In this regard, characteristics of water surface with wind-generated waves and roughness can change in a short time depending on wind variability. Moreover, the directional feature of waves causes differences in backscatter at different azimuth angles in data collected even within the same orbit. These backscatter variations invalidate the fundamental requirement of backscatter invariant for the deconvolution approach to be applicable. Therefore, a new method is necessary to account for backscatter change in the process to construct high-resolution data.
In view of the above, what is needed is the capability to obtain high-resolution wind climatology for offshore wind energy applications (that account for backscatter change) and for use in other scientific research.